v3

CMakeLists.txt

@@ -1,5 +1,5 @@
 cmake_minimum_required(VERSION 3.25)
-project(COSC-4P80-Final-Project VERSION 0.0.24)
+project(COSC-4P80-Final-Project VERSION 0.0.25)
 
 option(ENABLE_ADDRSAN "Enable the address sanitizer" OFF)
 option(ENABLE_UBSAN "Enable the ub sanitizer" OFF)

latex/chapters/abstract.aux

@@ -0,0 +1,44 @@
+\relax 
+\providecommand{\transparent@use}[1]{}
+\providecommand\hyper@newdestlabel[2]{}
+\@setckpt{chapters/abstract}{
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+}

latex/chapters/abstract.tex

@@ -0,0 +1,2 @@
+\chapter*{Abstract}
+Deep Learning folds feature extraction into the traditional neural network architecture. This paper will use the MNIST handwritten digit dataset to show the benefit in using deep learning techniques. 

latex/chapters/conclusion.aux

@@ -0,0 +1,44 @@
+\relax 
+\providecommand{\transparent@use}[1]{}
+\providecommand\hyper@newdestlabel[2]{}
+\@setckpt{chapters/conclusion}{
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+}

latex/chapters/introduction_and_motivation.aux

@@ -0,0 +1,58 @@
+\relax 
+\providecommand{\transparent@use}[1]{}
+\providecommand\hyper@newdestlabel[2]{}
+\@writefile{toc}{\contentsline {chapter}{\numberline {1}Introduction}{1}{chapter.1}\protected@file@percent }
+\@writefile{lof}{\addvspace {10\p@ }}
+\@writefile{lot}{\addvspace {10\p@ }}
+\@writefile{toc}{\contentsline {paragraph}{}{1}{section*.4}\protected@file@percent }
+\@writefile{toc}{\contentsline {paragraph}{}{1}{section*.5}\protected@file@percent }
+\@writefile{toc}{\contentsline {paragraph}{}{1}{section*.6}\protected@file@percent }
+\@writefile{toc}{\contentsline {chapter}{\numberline {2}Experimental Setup}{2}{chapter.2}\protected@file@percent }
+\@writefile{lof}{\addvspace {10\p@ }}
+\@writefile{lot}{\addvspace {10\p@ }}
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+\@writefile{toc}{\contentsline {section}{\numberline {2.1}Experiment 1}{2}{section.2.1}\protected@file@percent }
+\@writefile{toc}{\contentsline {paragraph}{}{2}{section*.8}\protected@file@percent }
+\@writefile{toc}{\contentsline {section}{\numberline {2.2}Experiment 2}{2}{section.2.2}\protected@file@percent }
+\@writefile{toc}{\contentsline {paragraph}{}{2}{section*.9}\protected@file@percent }
+\@setckpt{chapters/introduction_and_motivation}{
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+}

latex/chapters/introduction_and_motivation.tex

@@ -0,0 +1,17 @@
+\chapter{Introduction}
+\paragraph{}As previously mentioned, deep learning combines feature extraction through convolution and pooling with traditional neural networks, eliminating the need for humans to manually extract features from datasets. Convolution, in essence, is a filtering process where trained filter(s) slides over the input data to extract features and other useful information. Pooling is the subsequent process of taking local samples and selecting either the minimum, maximum, or average of those samples. This step helps identify feature locations and condenses the information produced by the convolution layer.
+
+\paragraph{}A typical deep learning pipeline consists of several convolution and pooling layers, followed by a few fully connected layers. In this work, we aim to demonstrate that using a deep learning configuration can reduce the size of the feed-forward section without compromising program performance, thereby highlighting the effectiveness of deep learning.
+
+\paragraph{}The MNIST database is a standard benchmark for image-processing neural networks. For our comparison, we will use a modified version of the DLIB deep learning example. This approach allows us to showcase the differences between standard feed-forward neural networks and deep learning networks without requiring expensive GPUs or AI accelerators. While the MNIST dataset is solvable using feed-forward neural networks, we intend to demonstrate that deep learning can achieve better classification performance, even on smaller networks.
+
+\chapter{Experimental Setup}
+\paragraph{}
+Our experiments are divided into two parts, each testing a deep learning network alongside its corresponding feed-forward network. For a fair comparison, the feed-forward test focuses explicitly on the feed-forward component of the deep learning network. This ensures that variables such as the number of layers or nodes in the feed-forward section remain consistent, minimizing potential biases and maintaining the integrity of our comparisons.
+
+\section{Experiment 1}
+\paragraph{}
+Our first experiment compares using the included example from the DLIB C++ library. Specifically the deep learning test consists of 
+
+\section{Experiment 2}
+\paragraph{}

latex/chapters/results.aux

@@ -0,0 +1,44 @@
+\relax 
+\providecommand{\transparent@use}[1]{}
+\providecommand\hyper@newdestlabel[2]{}
+\@setckpt{chapters/results}{
+\setcounter{page}{3}
+\setcounter{equation}{0}
+\setcounter{enumi}{0}
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+}

latex/chapters/results.tex

@@ -0,0 +1 @@
+ 

latex/report.aux

@@ -0,0 +1,24 @@
+\relax 
+\providecommand{\transparent@use}[1]{}
+\AC@reset@newl@bel
+\providecommand\hyper@newdestlabel[2]{}
+\providecommand\HyperFirstAtBeginDocument{\AtBeginDocument}
+\HyperFirstAtBeginDocument{\ifx\hyper@anchor\@undefined
+\global\let\oldnewlabel\newlabel
+\gdef\newlabel#1#2{\newlabelxx{#1}#2}
+\gdef\newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}}
+\AtEndDocument{\ifx\hyper@anchor\@undefined
+\let\newlabel\oldnewlabel
+\fi}
+\fi}
+\global\let\hyper@last\relax 
+\gdef\HyperFirstAtBeginDocument#1{#1}
+\providecommand\HyField@AuxAddToFields[1]{}
+\providecommand\HyField@AuxAddToCoFields[2]{}
+\@input{chapters/abstract.aux}
+\@writefile{toc}{\contentsline {chapter}{Table of Contents}{II}{section*.2}\protected@file@percent }
+\@input{chapters/introduction_and_motivation.aux}
+\@input{chapters/results.aux}
+\@input{chapters/conclusion.aux}
+\gdef\svg@ink@ver@settings{{\m@ne }{inkscape}{\m@ne }}
+\gdef \@abspage@last{5}

latex/report.out

@@ -0,0 +1,5 @@
+\BOOKMARK [0][-]{section*.2}{\376\377\000T\000a\000b\000l\000e\000\040\000o\000f\000\040\000C\000o\000n\000t\000e\000n\000t\000s}{}% 1
+\BOOKMARK [0][-]{chapter.1}{\376\377\000I\000n\000t\000r\000o\000d\000u\000c\000t\000i\000o\000n}{}% 2
+\BOOKMARK [0][-]{chapter.2}{\376\377\000E\000x\000p\000e\000r\000i\000m\000e\000n\000t\000a\000l\000\040\000S\000e\000t\000u\000p}{}% 3
+\BOOKMARK [1][-]{section.2.1}{\376\377\000E\000x\000p\000e\000r\000i\000m\000e\000n\000t\000\040\0001}{chapter.2}% 4
+\BOOKMARK [1][-]{section.2.2}{\376\377\000E\000x\000p\000e\000r\000i\000m\000e\000n\000t\000\040\0002}{chapter.2}% 5

latex/report.tex

@@ -0,0 +1,111 @@
+\documentclass[12pt,a4paper]{report}
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{ragged2e}
+\usepackage{arabtex}
+\usepackage{utf8}
+\usepackage{amsmath}
+\usepackage{amsfonts}
+\usepackage{svg}
+\usepackage{subcaption}
+\usepackage{acronym}
+\usepackage{algorithm}
+\usepackage{algpseudocode}
+\usepackage[noadjust]{cite}
+\usepackage[section]{placeins}
+\usepackage{mathtools}
+\usepackage{xcolor}
+
+\usepackage{datetime}
+\usepackage{longtable}
+\usepackage{tabularx}
+\usepackage{graphicx}
+\usepackage{caption}
+\usepackage{subcaption}
+\usepackage{float}
+
+\newcommand{\Var}[1]{\textcolor{blue}{#1}} % Color variables in blue
+
+\usepackage[hyperfootnotes,hidelinks]{hyperref}
+\hypersetup{
+	linktoc=all
+}
+
+\renewcommand{\contentsname}{Table of Contents}
+
+%----------EDIT COVER INFO HERE -----------------%
+
+\def \LOGOPATH {assets/brock.jpg}
+\def \UNIVERSITY {Brock University}
+\def \FACULTY {Faculty of Mathematics \& Science}
+\def \DEPARTEMENT {Department of Computer Science}
+\def \PROJECTTITLE {COSC 4P80 Final Project}
+\def \PROJECTSUBTITLE {A Comparative Analysis of Deep Learning and Feed-Forward Neural Networks}
+\def \STUDENTA {Brett Terpstra}
+\def \STUDENTB {Steve Mastrokalos}
+\def \STUDENTNOA {6920201}
+\def \STUDENTNOB {7276900}
+\def \STUDENTEMAILA {bt19ex@brocku.ca}
+\def \STUDENTEMAILB {sm21ks@brocku.ca}
+\def \SUPERVISOR {Dave Bockus}
+
+%------------------------------------------------%
+
+\newdateformat{monthyeardate}{%
+	\monthname[\THEMONTH], \THEYEAR}
+
+\begin{document}
+	\setlength{\parindent}{0em}
+	\setlength{\parskip}{0.5em}
+	
+	\pagenumbering{Roman}
+	
+	
+	\begin{titlepage}
+		\vfill
+		\begin{center}
+			\includegraphics[width=0.6\textwidth]{\LOGOPATH} \\
+			\fontsize{14pt}{14pt}\selectfont
+			\vfill
+			\UNIVERSITY \\
+			\FACULTY \\
+			\DEPARTEMENT \\
+			\vfill
+			\fontsize{18pt}{18pt}\selectfont
+			\textbf{\PROJECTTITLE}
+			\vfill
+			\fontsize{14pt}{14pt}\selectfont
+			Prepared By: \\
+			\STUDENTA \ \#\STUDENTNOA \\
+			\STUDENTEMAILA \\
+			\vfill
+			\STUDENTB \ \#\STUDENTNOB \\
+			\STUDENTEMAILB \\
+			\vfill
+			Instructor: \\
+			\SUPERVISOR
+			\vfill
+			\vfill
+			\vfill
+			\monthyeardate\today
+		\end{center}
+	\end{titlepage}
+	
+	\include{chapters/abstract}
+
+	\cleardoublepage \phantomsection \addcontentsline{toc}{chapter}{Table of Contents}
+	\tableofcontents
+	
+	\cleardoublepage
+	
+	\setlength{\parindent}{0em}
+	\setlength{\parskip}{0.5em}
+	
+	\pagenumbering{arabic}
+	
+	\include{chapters/introduction_and_motivation}
+	
+	\include{chapters/results}
+	
+	\include{chapters/conclusion}
+\end{document}

latex/report.toc

@@ -0,0 +1,11 @@
+\contentsline {chapter}{Table of Contents}{II}{section*.2}%
+\contentsline {chapter}{\numberline {1}Introduction}{1}{chapter.1}%
+\contentsline {paragraph}{}{1}{section*.4}%
+\contentsline {paragraph}{}{1}{section*.5}%
+\contentsline {paragraph}{}{1}{section*.6}%
+\contentsline {chapter}{\numberline {2}Experimental Setup}{2}{chapter.2}%
+\contentsline {paragraph}{}{2}{section*.7}%
+\contentsline {section}{\numberline {2.1}Experiment 1}{2}{section.2.1}%
+\contentsline {paragraph}{}{2}{section*.8}%
+\contentsline {section}{\numberline {2.2}Experiment 2}{2}{section.2.2}%
+\contentsline {paragraph}{}{2}{section*.9}%

part2/average_epochs.txt

@@ -0,0 +1 @@
+1000,1000

part2/deep_train_results.csv

@@ -0,0 +1,101 @@
+Epoch,Loss
+0,0.73787
+1,0.448052
+2,0.33467
+3,0.0876439
+4,0.0816766
+5,0.0768863
+6,0.0610655
+7,0.0585332
+8,0.0562475
+9,0.0481311
+10,0.0465637
+11,0.0451581
+12,0.0400062
+13,0.0389325
+14,0.037937
+15,0.0342676
+16,0.0334845
+17,0.0327614
+18,0.0300113
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+95,0.00564002
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+99,0.00531278

part2/forward_train_results.csv

@@ -0,0 +1,101 @@
+Epoch,Loss
+0,0.976437
+1,0.674343
+2,0.552924
+3,0.483993
+4,0.438136
+5,0.404714
+6,0.378856
+7,0.358012
+8,0.34071
+9,0.326037
+10,0.313377
+11,0.302307
+12,0.29252
+13,0.283781
+14,0.275915
+15,0.268783
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+17,0.256309
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+27,0.21513
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+99,0.135109

part2/test_results_table.txt

@@ -0,0 +1,9 @@
+\begin{figure}
+	\begin{tabular}{|c|c|c|c|}
+		\hline
+		Test & Correct & Incorrect & Accuracy (\%) \\
+		\hline
+		Feed-Forward & 9588 & 411 & 96\\
+		Deep Learning & 9887 & 112 & 99\\
+	\end{tabular}
+\end{figure}

src/MNIST.cpp

@@ -80,35 +80,35 @@ namespace fp
 
             switch (magic_arr[2])
             {
-            // unsigned char
+                // unsigned char
             case 0x08:
                 data = mk_v<blt::u8>{};
                 read_data<blt::u8>(file, total_size);
                 break;
-            // signed char
+                // signed char
             case 0x09:
                 data = mk_v<blt::i8>{};
                 read_data<blt::i8>(file, total_size);
                 break;
-            // short
+                // short
             case 0x0B:
                 data = mk_v<blt::u16>{};
                 read_data<blt::u16>(file, total_size);
                 reverse_data<blt::u16>();
                 break;
-            // int
+                // int
             case 0x0C:
                 data = mk_v<blt::u32>{};
                 read_data<blt::u32>(file, total_size);
                 reverse_data<blt::u32>();
                 break;
-            // float
+                // float
             case 0x0D:
                 data = mk_v<blt::f32>{};
                 read_data<blt::f32>(file, total_size);
                 reverse_data<blt::f32>();
                 break;
-            // double
+                // double
             case 0x0E:
                 data = mk_v<blt::f64>{};
                 read_data<blt::f64>(file, total_size);
@@ -598,10 +598,10 @@ namespace fp
                 {
                     network = load_network<NetworkType>(local_ident);
                 }
-                catch (dlib::serialization_error&)
-                {
-                    goto train_label;
-                }
+            catch (dlib::serialization_error&)
+            {
+                goto train_label;
+            }
             else
             {
                 train_label:
@@ -712,6 +712,34 @@ namespace fp
         return run_network_tests<net_type_ff>(path, "feed_forward", runs, restore);
     }
 
+    auto run_deep_learning_tests_v3(const std::string& path, const blt::i32 runs, const bool restore)
+    {
+        using namespace dlib;
+        using net_type_dl = loss_multiclass_log<
+            fc<10,
+               relu<fc<6,
+                       relu<fc<6,
+                               max_pool<2, 2, 2, 2, relu<con<16, 5, 5, 1, 1,
+                                                             max_pool<2, 2, 2, 2, relu<con<6, 5, 5, 1, 1,
+                                                                                           input<matrix<blt::u8>>>>>>>>>>>>>>;
+        BLT_TRACE("Running deep learning tests");
+        return run_network_tests<net_type_dl>(path, "deep_learning", runs, restore);
+    }
+
+    auto run_feed_forward_tests_v3(const std::string& path, const blt::i32 runs, const bool restore)
+    {
+        using namespace dlib;
+
+        using net_type_ff = loss_multiclass_log<
+            fc<10,
+               relu<fc<6,
+                       relu<fc<6,
+                               input<matrix<blt::u8>>>>>>>>;
+
+        BLT_TRACE("Running feed forward tests");
+        return run_network_tests<net_type_ff>(path, "feed_forward", runs, restore);
+    }
+
     void run_mnist(const int argc, const char** argv)
     {
         binary_directory = std::filesystem::current_path();
@@ -785,9 +813,11 @@ namespace fp
         auto path = binary_directory + args.get<std::string>("network");
 
         // auto [deep_stats, deep_tests] = run_deep_learning_tests(path, runs, restore);
-        auto [forward_stats, forward_tests] = run_feed_forward_tests_v2(path, runs, restore);
-        auto [deep_stats, deep_tests] = run_deep_learning_tests_v2(path, runs, restore);
+        // auto [deep_stats, deep_tests] = run_deep_learning_tests_v2(path, runs, restore);
+        auto [deep_stats, deep_tests] = run_deep_learning_tests_v3(path, runs, restore);
         // auto [forward_stats, forward_tests] = run_feed_forward_tests(path, runs, restore);
+        // auto [forward_stats, forward_tests] = run_feed_forward_tests_v2(path, runs, restore);
+        auto [forward_stats, forward_tests] = run_feed_forward_tests_v3(path, runs, restore);
 
         auto average_forward_size = forward_stats.average_size();
         auto average_deep_size = deep_stats.average_size();